{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Introduction to DataFrames\n",
    "**[Bogumił Kamiński](http://bogumilkaminski.pl/about/), November 20, 2020**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "using DataFrames"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Load and save DataFrames\n",
    "We do not cover all features of the packages. Please refer to their documentation to learn them.\n",
    "\n",
    "Here we'll load CSV.jl to read and write CSV files and Arrow.jl, JLSO.jl, and serialization, which allow us to work with a binary format and JSONTables.jl for JSON interaction. Finally we consider a custom JDF.jl format."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "using Arrow\n",
    "using CSV\n",
    "using Serialization\n",
    "using JLSO\n",
    "using JSONTables\n",
    "using CodecZlib\n",
    "using ZipFile\n",
    "using JDF\n",
    "using StatsPlots # for charts\n",
    "using Mmap # for compression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a simple `DataFrame` for testing purposes,"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>A</th><th>B</th><th>C</th><th>D</th></tr><tr><th></th><th>Bool</th><th>Int64?</th><th>String?</th><th>Char?</th></tr></thead><tbody><p>3 rows × 4 columns</p><tr><th>1</th><td>1</td><td>1</td><td><em>missing</em></td><td>a</td></tr><tr><th>2</th><td>0</td><td>2</td><td>b</td><td><em>missing</em></td></tr><tr><th>3</th><td>1</td><td><em>missing</em></td><td>c</td><td>c</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& A & B & C & D\\\\\n",
       "\t\\hline\n",
       "\t& Bool & Int64? & String? & Char?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & \\emph{missing} & a \\\\\n",
       "\t2 & 0 & 2 & b & \\emph{missing} \\\\\n",
       "\t3 & 1 & \\emph{missing} & c & c \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m B       \u001b[0m\u001b[1m C       \u001b[0m\u001b[1m D       \u001b[0m\n",
       "\u001b[1m     \u001b[0m│\u001b[90m Bool  \u001b[0m\u001b[90m Int64?  \u001b[0m\u001b[90m String? \u001b[0m\u001b[90m Char?   \u001b[0m\n",
       "─────┼──────────────────────────────────\n",
       "   1 │  true        1 \u001b[90m missing \u001b[0m a\n",
       "   2 │ false        2  b       \u001b[90m missing \u001b[0m\n",
       "   3 │  true \u001b[90m missing \u001b[0m c        c"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = DataFrame(A=[true, false, true], B=[1, 2, missing],\n",
    "              C=[missing, \"b\", \"c\"], D=['a', missing, 'c'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "and use `eltypes` to look at the columnwise types."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Array{Type,1}:\n",
       " Bool\n",
       " Union{Missing, Int64}\n",
       " Union{Missing, String}\n",
       " Union{Missing, Char}"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eltype.(eachcol(x))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### CSV.jl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's use `CSV` to save `x` to disk; make sure `x1.csv` does not conflict with some file in your working directory."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"x1.csv\""
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "CSV.write(\"x1.csv\", x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can see how it was saved by reading `x.csv`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "A,B,C,D\n",
      "true,1,,a\n",
      "false,2,b,\n",
      "true,,c,c\n"
     ]
    }
   ],
   "source": [
    "print(read(\"x1.csv\", String))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also load it back (`use_mmap=false` disables memory mapping so that on Windows the file can be deleted in the same session, on other OSs it is not needed)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>A</th><th>B</th><th>C</th><th>D</th></tr><tr><th></th><th>Bool</th><th>Int64?</th><th>String?</th><th>String?</th></tr></thead><tbody><p>3 rows × 4 columns</p><tr><th>1</th><td>1</td><td>1</td><td><em>missing</em></td><td>a</td></tr><tr><th>2</th><td>0</td><td>2</td><td>b</td><td><em>missing</em></td></tr><tr><th>3</th><td>1</td><td><em>missing</em></td><td>c</td><td>c</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& A & B & C & D\\\\\n",
       "\t\\hline\n",
       "\t& Bool & Int64? & String? & String?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & \\emph{missing} & a \\\\\n",
       "\t2 & 0 & 2 & b & \\emph{missing} \\\\\n",
       "\t3 & 1 & \\emph{missing} & c & c \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m B       \u001b[0m\u001b[1m C       \u001b[0m\u001b[1m D       \u001b[0m\n",
       "\u001b[1m     \u001b[0m│\u001b[90m Bool  \u001b[0m\u001b[90m Int64?  \u001b[0m\u001b[90m String? \u001b[0m\u001b[90m String? \u001b[0m\n",
       "─────┼──────────────────────────────────\n",
       "   1 │  true        1 \u001b[90m missing \u001b[0m a\n",
       "   2 │ false        2  b       \u001b[90m missing \u001b[0m\n",
       "   3 │  true \u001b[90m missing \u001b[0m c        c"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = CSV.read(\"x1.csv\", DataFrame)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that when loading in a `DataFrame` from a `CSV` the column type for column `:D` has changed!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Array{Type,1}:\n",
       " Bool\n",
       " Union{Missing, Int64}\n",
       " Union{Missing, String}\n",
       " Union{Missing, String}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eltype.(eachcol(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Serialization, JDF.jl, and JLSO.jl\n",
    "\n",
    "#### Serialization"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we use serialization to save `x`.\n",
    "\n",
    "There are two ways to perform serialization. The first way is to use the `Serialization.serialize` as below:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that in general, this process will not work if the reading and writing are done by different versions of Julia, or an instance of Julia with a different system image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "open(\"x.bin\", \"w\") do io\n",
    "    serialize(io, x)\n",
    "end"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we load back the saved file to `y` variable. Again `y` is identical to `x`. However, please beware that if you session does not have DataFrames.jl loaded, then it may not recognise the content as DataFrames.jl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>A</th><th>B</th><th>C</th><th>D</th></tr><tr><th></th><th>Bool</th><th>Int64?</th><th>String?</th><th>Char?</th></tr></thead><tbody><p>3 rows × 4 columns</p><tr><th>1</th><td>1</td><td>1</td><td><em>missing</em></td><td>a</td></tr><tr><th>2</th><td>0</td><td>2</td><td>b</td><td><em>missing</em></td></tr><tr><th>3</th><td>1</td><td><em>missing</em></td><td>c</td><td>c</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& A & B & C & D\\\\\n",
       "\t\\hline\n",
       "\t& Bool & Int64? & String? & Char?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & \\emph{missing} & a \\\\\n",
       "\t2 & 0 & 2 & b & \\emph{missing} \\\\\n",
       "\t3 & 1 & \\emph{missing} & c & c \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m B       \u001b[0m\u001b[1m C       \u001b[0m\u001b[1m D       \u001b[0m\n",
       "\u001b[1m     \u001b[0m│\u001b[90m Bool  \u001b[0m\u001b[90m Int64?  \u001b[0m\u001b[90m String? \u001b[0m\u001b[90m Char?   \u001b[0m\n",
       "─────┼──────────────────────────────────\n",
       "   1 │  true        1 \u001b[90m missing \u001b[0m a\n",
       "   2 │ false        2  b       \u001b[90m missing \u001b[0m\n",
       "   3 │  true \u001b[90m missing \u001b[0m c        c"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = open(deserialize, \"x.bin\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Array{Type,1}:\n",
       " Bool\n",
       " Union{Missing, Int64}\n",
       " Union{Missing, String}\n",
       " Union{Missing, Char}"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eltype.(eachcol(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### JDF.jl\n",
    "\n",
    "[JDF.jl](https://github.com/xiaodaigh/JDF) is a relatively new package designed to serialize DataFrames. You can save a DataFrame with the `savejdf` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "savejdf(\"x.jdf\", x);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To load the saved JDF file, one can use the `loadjdf` function"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>A</th><th>B</th><th>C</th><th>D</th></tr><tr><th></th><th>Bool</th><th>Int64?</th><th>String?</th><th>Char?</th></tr></thead><tbody><p>3 rows × 4 columns</p><tr><th>1</th><td>1</td><td>1</td><td><em>missing</em></td><td>a</td></tr><tr><th>2</th><td>0</td><td>2</td><td>b</td><td><em>missing</em></td></tr><tr><th>3</th><td>1</td><td><em>missing</em></td><td>c</td><td>c</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& A & B & C & D\\\\\n",
       "\t\\hline\n",
       "\t& Bool & Int64? & String? & Char?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & \\emph{missing} & a \\\\\n",
       "\t2 & 0 & 2 & b & \\emph{missing} \\\\\n",
       "\t3 & 1 & \\emph{missing} & c & c \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m B       \u001b[0m\u001b[1m C       \u001b[0m\u001b[1m D       \u001b[0m\n",
       "\u001b[1m     \u001b[0m│\u001b[90m Bool  \u001b[0m\u001b[90m Int64?  \u001b[0m\u001b[90m String? \u001b[0m\u001b[90m Char?   \u001b[0m\n",
       "─────┼──────────────────────────────────\n",
       "   1 │  true        1 \u001b[90m missing \u001b[0m a\n",
       "   2 │ false        2  b       \u001b[90m missing \u001b[0m\n",
       "   3 │  true \u001b[90m missing \u001b[0m c        c"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_loaded = loadjdf(\"x.jdf\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can see that they are the same"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "isequal(x_loaded, x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "JDF.jl offers the ability to load only certain columns from disk to help with working with large files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "JDFFile{String}(\"x.jdf\")"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set up a JDFFile which is a on disk representation of `x` backed by JDF.jl\n",
    "x_ondisk = jdf\"x.jdf\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see all the names of `x` without loading it into memory"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Array{String,1}:\n",
       " \"A\"\n",
       " \"B\"\n",
       " \"C\"\n",
       " \"D\""
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names(x_ondisk)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The below is an example of how to load only columns `:A` and `:D` "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>A</th><th>D</th></tr><tr><th></th><th>Bool</th><th>Char?</th></tr></thead><tbody><p>3 rows × 2 columns</p><tr><th>1</th><td>1</td><td>a</td></tr><tr><th>2</th><td>0</td><td><em>missing</em></td></tr><tr><th>3</th><td>1</td><td>c</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& A & D\\\\\n",
       "\t\\hline\n",
       "\t& Bool & Char?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & a \\\\\n",
       "\t2 & 0 & \\emph{missing} \\\\\n",
       "\t3 & 1 & c \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m D       \u001b[0m\n",
       "\u001b[1m     \u001b[0m│\u001b[90m Bool  \u001b[0m\u001b[90m Char?   \u001b[0m\n",
       "─────┼────────────────\n",
       "   1 │  true  a\n",
       "   2 │ false \u001b[90m missing \u001b[0m\n",
       "   3 │  true  c"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xd = sloadjdf(x_ondisk; cols = [\"A\", \"D\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### JDF.jl vs others\n",
    "\n",
    "JDF.jl is specialized to DataFrames.jl and only supports a restricted list of columns, so it can not save data frames with arbitrary column types. However, this also means that JDF.jl has specialised algorithms to serailize the type it supports to optimize speed, minimize disk usage, and reduce the chance of errors\n",
    "\n",
    "The list support columns for JDF include\n",
    "\n",
    "```julia\n",
    "WeakRefStrings.StringVector\n",
    "Vector{T}, Vector{Union{Mising, T}}, Vector{Union{Nothing, T}}\n",
    "CategoricalArrays.CategoricalVetors{T}\n",
    "```\n",
    "\n",
    "where `T` can be `String`, `Bool`, `Symbol`, `Char`, `TimeZones.ZonedDateTime` (experimental) and `isbit`s types i.e. `UInt*`, `Int*`, `Float*`, and `Date*` types etc."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### JLSO.jl\n",
    "\n",
    "Another way to perform serialization is by using the [JLSO.jl](https://github.com/invenia/JLSO.jl) library:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "JLSO.save(\"x.jlso\", :data => x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we can laod back the file to `y`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>A</th><th>B</th><th>C</th><th>D</th></tr><tr><th></th><th>Bool</th><th>Int64?</th><th>String?</th><th>Char?</th></tr></thead><tbody><p>3 rows × 4 columns</p><tr><th>1</th><td>1</td><td>1</td><td><em>missing</em></td><td>a</td></tr><tr><th>2</th><td>0</td><td>2</td><td>b</td><td><em>missing</em></td></tr><tr><th>3</th><td>1</td><td><em>missing</em></td><td>c</td><td>c</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& A & B & C & D\\\\\n",
       "\t\\hline\n",
       "\t& Bool & Int64? & String? & Char?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & \\emph{missing} & a \\\\\n",
       "\t2 & 0 & 2 & b & \\emph{missing} \\\\\n",
       "\t3 & 1 & \\emph{missing} & c & c \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m B       \u001b[0m\u001b[1m C       \u001b[0m\u001b[1m D       \u001b[0m\n",
       "\u001b[1m     \u001b[0m│\u001b[90m Bool  \u001b[0m\u001b[90m Int64?  \u001b[0m\u001b[90m String? \u001b[0m\u001b[90m Char?   \u001b[0m\n",
       "─────┼──────────────────────────────────\n",
       "   1 │  true        1 \u001b[90m missing \u001b[0m a\n",
       "   2 │ false        2  b       \u001b[90m missing \u001b[0m\n",
       "   3 │  true \u001b[90m missing \u001b[0m c        c"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = JLSO.load(\"x.jlso\")[:data]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Array{Type,1}:\n",
       " Bool\n",
       " Union{Missing, Int64}\n",
       " Union{Missing, String}\n",
       " Union{Missing, Char}"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eltype.(eachcol(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### JSONTables.jl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Often you might need to read and write data stored in JSON format. JSONTables.jl provides a way to process them in row-oriented or column-oriented layout. We present both options below."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "106"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open(io -> arraytable(io, x), \"x1.json\", \"w\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "76"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "open(io -> objecttable(io, x), \"x2.json\", \"w\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[{\"A\":true,\"B\":1,\"C\":null,\"D\":\"a\"},{\"A\":false,\"B\":2,\"C\":\"b\",\"D\":null},{\"A\":true,\"B\":null,\"C\":\"c\",\"D\":\"c\"}]"
     ]
    }
   ],
   "source": [
    "print(read(\"x1.json\", String))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{\"A\":[true,false,true],\"B\":[1,2,null],\"C\":[null,\"b\",\"c\"],\"D\":[\"a\",null,\"c\"]}"
     ]
    }
   ],
   "source": [
    "print(read(\"x2.json\", String))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>A</th><th>B</th><th>C</th><th>D</th></tr><tr><th></th><th>Bool</th><th>Int64?</th><th>String?</th><th>String?</th></tr></thead><tbody><p>3 rows × 4 columns</p><tr><th>1</th><td>1</td><td>1</td><td><em>missing</em></td><td>a</td></tr><tr><th>2</th><td>0</td><td>2</td><td>b</td><td><em>missing</em></td></tr><tr><th>3</th><td>1</td><td><em>missing</em></td><td>c</td><td>c</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& A & B & C & D\\\\\n",
       "\t\\hline\n",
       "\t& Bool & Int64? & String? & String?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & \\emph{missing} & a \\\\\n",
       "\t2 & 0 & 2 & b & \\emph{missing} \\\\\n",
       "\t3 & 1 & \\emph{missing} & c & c \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m B       \u001b[0m\u001b[1m C       \u001b[0m\u001b[1m D       \u001b[0m\n",
       "\u001b[1m     \u001b[0m│\u001b[90m Bool  \u001b[0m\u001b[90m Int64?  \u001b[0m\u001b[90m String? \u001b[0m\u001b[90m String? \u001b[0m\n",
       "─────┼──────────────────────────────────\n",
       "   1 │  true        1 \u001b[90m missing \u001b[0m a\n",
       "   2 │ false        2  b       \u001b[90m missing \u001b[0m\n",
       "   3 │  true \u001b[90m missing \u001b[0m c        c"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y1 = open(jsontable, \"x1.json\") |> DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Array{Type,1}:\n",
       " Bool\n",
       " Union{Missing, Int64}\n",
       " Union{Missing, String}\n",
       " Union{Missing, String}"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eltype.(eachcol(y1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>A</th><th>B</th><th>C</th><th>D</th></tr><tr><th></th><th>Bool</th><th>Int64?</th><th>String?</th><th>String?</th></tr></thead><tbody><p>3 rows × 4 columns</p><tr><th>1</th><td>1</td><td>1</td><td><em>missing</em></td><td>a</td></tr><tr><th>2</th><td>0</td><td>2</td><td>b</td><td><em>missing</em></td></tr><tr><th>3</th><td>1</td><td><em>missing</em></td><td>c</td><td>c</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& A & B & C & D\\\\\n",
       "\t\\hline\n",
       "\t& Bool & Int64? & String? & String?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & \\emph{missing} & a \\\\\n",
       "\t2 & 0 & 2 & b & \\emph{missing} \\\\\n",
       "\t3 & 1 & \\emph{missing} & c & c \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m B       \u001b[0m\u001b[1m C       \u001b[0m\u001b[1m D       \u001b[0m\n",
       "\u001b[1m     \u001b[0m│\u001b[90m Bool  \u001b[0m\u001b[90m Int64?  \u001b[0m\u001b[90m String? \u001b[0m\u001b[90m String? \u001b[0m\n",
       "─────┼──────────────────────────────────\n",
       "   1 │  true        1 \u001b[90m missing \u001b[0m a\n",
       "   2 │ false        2  b       \u001b[90m missing \u001b[0m\n",
       "   3 │  true \u001b[90m missing \u001b[0m c        c"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2 = open(jsontable, \"x2.json\") |> DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Array{Type,1}:\n",
       " Bool\n",
       " Union{Missing, Int64}\n",
       " Union{Missing, String}\n",
       " Union{Missing, String}"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eltype.(eachcol(y2))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Arrow.jl"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally we use Apache Arrow format that allows, in particular, for data interchange with R or Python."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "\"x.arrow\""
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Arrow.write(\"x.arrow\", x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>A</th><th>B</th><th>C</th><th>D</th></tr><tr><th></th><th>Bool</th><th>Int64?</th><th>String?</th><th>Char?</th></tr></thead><tbody><p>3 rows × 4 columns</p><tr><th>1</th><td>1</td><td>1</td><td><em>missing</em></td><td>a</td></tr><tr><th>2</th><td>0</td><td>2</td><td>b</td><td><em>missing</em></td></tr><tr><th>3</th><td>1</td><td><em>missing</em></td><td>c</td><td>c</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& A & B & C & D\\\\\n",
       "\t\\hline\n",
       "\t& Bool & Int64? & String? & Char?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & \\emph{missing} & a \\\\\n",
       "\t2 & 0 & 2 & b & \\emph{missing} \\\\\n",
       "\t3 & 1 & \\emph{missing} & c & c \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m B       \u001b[0m\u001b[1m C       \u001b[0m\u001b[1m D       \u001b[0m\n",
       "\u001b[1m     \u001b[0m│\u001b[90m Bool  \u001b[0m\u001b[90m Int64?  \u001b[0m\u001b[90m String? \u001b[0m\u001b[90m Char?   \u001b[0m\n",
       "─────┼──────────────────────────────────\n",
       "   1 │  true        1 \u001b[90m missing \u001b[0m a\n",
       "   2 │ false        2  b       \u001b[90m missing \u001b[0m\n",
       "   3 │  true \u001b[90m missing \u001b[0m c        c"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = Arrow.Table(\"x.arrow\") |> DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4-element Array{Type,1}:\n",
       " Bool\n",
       " Union{Missing, Int64}\n",
       " Union{Missing, String}\n",
       " Union{Missing, Char}"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "eltype.(eachcol(y))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that columns of `y` are immutable"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "ename": "LoadError",
     "evalue": "ReadOnlyMemoryError()",
     "output_type": "error",
     "traceback": [
      "ReadOnlyMemoryError()",
      "",
      "Stacktrace:",
      " [1] setindex! at .\\array.jl:847 [inlined]",
      " [2] setindex!(::Arrow.BoolVector{Bool}, ::Bool, ::Int64) at D:\\AppData\\.julia\\packages\\Arrow\\Gl8iW\\src\\arraytypes\\bool.jl:49",
      " [3] top-level scope at In[32]:1",
      " [4] include_string(::Function, ::Module, ::String, ::String) at .\\loading.jl:1091"
     ]
    }
   ],
   "source": [
    "y.A[1] = false"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is because `Arrow.Table` uses memory mapping and thus uses a custom vector types:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Arrow.BoolVector{Bool}:\n",
       " 1\n",
       " 0\n",
       " 1"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Arrow.Primitive{Union{Missing, Int64},Array{Int64,1}}:\n",
       " 1\n",
       " 2\n",
       "  missing"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can get standard Julia Base vectors by copying a data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>A</th><th>B</th><th>C</th><th>D</th></tr><tr><th></th><th>Bool</th><th>Int64?</th><th>String?</th><th>Char?</th></tr></thead><tbody><p>3 rows × 4 columns</p><tr><th>1</th><td>1</td><td>1</td><td><em>missing</em></td><td>a</td></tr><tr><th>2</th><td>0</td><td>2</td><td>b</td><td><em>missing</em></td></tr><tr><th>3</th><td>1</td><td><em>missing</em></td><td>c</td><td>c</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& A & B & C & D\\\\\n",
       "\t\\hline\n",
       "\t& Bool & Int64? & String? & Char?\\\\\n",
       "\t\\hline\n",
       "\t1 & 1 & 1 & \\emph{missing} & a \\\\\n",
       "\t2 & 0 & 2 & b & \\emph{missing} \\\\\n",
       "\t3 & 1 & \\emph{missing} & c & c \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m A     \u001b[0m\u001b[1m B       \u001b[0m\u001b[1m C       \u001b[0m\u001b[1m D       \u001b[0m\n",
       "\u001b[1m     \u001b[0m│\u001b[90m Bool  \u001b[0m\u001b[90m Int64?  \u001b[0m\u001b[90m String? \u001b[0m\u001b[90m Char?   \u001b[0m\n",
       "─────┼──────────────────────────────────\n",
       "   1 │  true        1 \u001b[90m missing \u001b[0m a\n",
       "   2 │ false        2  b       \u001b[90m missing \u001b[0m\n",
       "   3 │  true \u001b[90m missing \u001b[0m c        c"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2 = copy(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Array{Bool,1}:\n",
       " 1\n",
       " 0\n",
       " 1"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2.A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3-element Array{Union{Missing, Int64},1}:\n",
       " 1\n",
       " 2\n",
       "  missing"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y2.B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Basic bechmarking"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, we'll create some files, so be careful that you don't already have these files in your working directory!\n",
    "\n",
    "In particular, we'll time how long it takes us to write a `DataFrame` with 10^3 rows and 10^5 columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First run\n",
      "CSV.jl\n",
      " 18.336468 seconds (251.61 M allocations: 8.272 GiB, 8.55% gc time)\n",
      "Serialization\n",
      "  1.292221 seconds (281.32 k allocations: 11.391 MiB)\n",
      "JDF.jl\n",
      "  3.174406 seconds (98.22 k allocations: 167.224 MiB, 0.84% gc time)\n",
      "JLSO.jl\n",
      "  4.622379 seconds (364.15 k allocations: 31.849 MiB)\n",
      "Arrow.jl\n",
      "  1.714735 seconds (3.91 M allocations: 210.636 MiB, 1.96% gc time)\n",
      "JSONTables.jl arraytable\n",
      "144.985897 seconds (1.30 G allocations: 227.096 GiB, 13.14% gc time)\n",
      "JSONTables.jl objecttable\n",
      "  2.078348 seconds (363.09 k allocations: 1.587 GiB, 4.75% gc time)\n",
      "Second run\n",
      "CSV.jl\n",
      " 17.975779 seconds (249.94 M allocations: 8.199 GiB, 6.94% gc time)\n",
      "Serialization\n",
      "  1.281253 seconds (105.69 k allocations: 2.292 MiB)\n",
      "JDF.jl\n",
      "  2.195526 seconds (27.08 k allocations: 163.650 MiB)\n",
      "JLSO.jl\n",
      "  4.601957 seconds (139.77 k allocations: 20.918 MiB)\n",
      "Arrow.jl\n",
      "  0.405740 seconds (123.71 k allocations: 14.419 MiB, 8.80% gc time)\n",
      "JSONTables.jl arraytable\n",
      "149.474199 seconds (1.30 G allocations: 227.064 GiB, 11.89% gc time)\n",
      "JSONTables.jl objecttable\n",
      "  1.410956 seconds (12.81 k allocations: 1.571 GiB, 6.22% gc time)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "1.413189399"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bigdf = DataFrame(rand(Bool, 10^5, 500), :auto)\n",
    "bigdf[!, 1] = Int.(bigdf[!, 1])\n",
    "bigdf[!, 2] = bigdf[!, 2] .+ 0.5\n",
    "bigdf[!, 3] = string.(bigdf[!, 3], \", as string\")\n",
    "println(\"First run\")\n",
    "println(\"CSV.jl\")\n",
    "csvwrite1 = @elapsed @time CSV.write(\"bigdf1.csv\", bigdf)\n",
    "println(\"Serialization\")\n",
    "serializewrite1 = @elapsed @time open(io -> serialize(io, bigdf), \"bigdf.bin\", \"w\")\n",
    "println(\"JDF.jl\")\n",
    "jdfwrite1 = @elapsed @time savejdf(\"bigdf.jdf\", bigdf)\n",
    "println(\"JLSO.jl\")\n",
    "jlsowrite1 = @elapsed @time JLSO.save(\"bigdf.jlso\", :data => bigdf)\n",
    "println(\"Arrow.jl\")\n",
    "arrowwrite1 = @elapsed @time Arrow.write(\"bigdf.arrow\", bigdf)\n",
    "println(\"JSONTables.jl arraytable\")\n",
    "jsontablesawrite1 = @elapsed @time open(io -> arraytable(io, bigdf), \"bigdf1.json\", \"w\")\n",
    "println(\"JSONTables.jl objecttable\")\n",
    "jsontablesowrite1 = @elapsed @time open(io -> objecttable(io, bigdf), \"bigdf2.json\", \"w\")\n",
    "println(\"Second run\")\n",
    "println(\"CSV.jl\")\n",
    "csvwrite2 = @elapsed @time CSV.write(\"bigdf1.csv\", bigdf)\n",
    "println(\"Serialization\")\n",
    "serializewrite2 = @elapsed @time open(io -> serialize(io, bigdf), \"bigdf.bin\", \"w\")\n",
    "println(\"JDF.jl\")\n",
    "jdfwrite2 = @elapsed @time savejdf(\"bigdf.jdf\", bigdf)\n",
    "println(\"JLSO.jl\")\n",
    "jlsowrite2 = @elapsed @time JLSO.save(\"bigdf.jlso\", :data => bigdf)\n",
    "println(\"Arrow.jl\")\n",
    "arrowwrite2 = @elapsed @time Arrow.write(\"bigdf.arrow\", bigdf)\n",
    "println(\"JSONTables.jl arraytable\")\n",
    "jsontablesawrite2 = @elapsed @time open(io -> arraytable(io, bigdf), \"bigdf1.json\", \"w\")\n",
    "println(\"JSONTables.jl objecttable\")\n",
    "jsontablesowrite2 = @elapsed @time open(io -> objecttable(io, bigdf), \"bigdf2.json\", \"w\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
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      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupedbar(\n",
    "    # Exclude JSONTables.jl arraytable due to timing\n",
    "    repeat([\"CSV.jl\", \"Serialization\", \"JDF.jl\", \"JLSO.jl\", \"Arrow.jl\", \"JSONTables.jl\\nobjecttable\"],\n",
    "            inner = 2),\n",
    "    [csvwrite1, csvwrite2, serializewrite1, serializewrite1, jdfwrite1, jdfwrite2,\n",
    "     jlsowrite1, jlsowrite2, arrowwrite1, arrowwrite2, jsontablesowrite2, jsontablesowrite2],\n",
    "    group = repeat([\"1st\", \"2nd\"], outer = 6),\n",
    "    ylab = \"Second\",\n",
    "    title = \"Write Performance\\nDataFrame: bigdf\\nSize: $(size(bigdf))\"\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>file</th><th>size</th></tr><tr><th></th><th>String</th><th>Int64</th></tr></thead><tbody><p>6 rows × 2 columns</p><tr><th>1</th><td>bigdf.arrow</td><td>9832362</td></tr><tr><th>2</th><td>bigdf.jdf</td><td>26010830</td></tr><tr><th>3</th><td>bigdf.bin</td><td>26765489</td></tr><tr><th>4</th><td>bigdf1.csv</td><td>275804508</td></tr><tr><th>5</th><td>bigdf2.json</td><td>275806509</td></tr><tr><th>6</th><td>bigdf1.json</td><td>615202117</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cc}\n",
       "\t& file & size\\\\\n",
       "\t\\hline\n",
       "\t& String & Int64\\\\\n",
       "\t\\hline\n",
       "\t1 & bigdf.arrow & 9832362 \\\\\n",
       "\t2 & bigdf.jdf & 26010830 \\\\\n",
       "\t3 & bigdf.bin & 26765489 \\\\\n",
       "\t4 & bigdf1.csv & 275804508 \\\\\n",
       "\t5 & bigdf2.json & 275806509 \\\\\n",
       "\t6 & bigdf1.json & 615202117 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m6×2 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m file        \u001b[0m\u001b[1m size      \u001b[0m\n",
       "\u001b[1m     \u001b[0m│\u001b[90m String      \u001b[0m\u001b[90m Int64     \u001b[0m\n",
       "─────┼────────────────────────\n",
       "   1 │ bigdf.arrow    9832362\n",
       "   2 │ bigdf.jdf     26010830\n",
       "   3 │ bigdf.bin     26765489\n",
       "   4 │ bigdf1.csv   275804508\n",
       "   5 │ bigdf2.json  275806509\n",
       "   6 │ bigdf1.json  615202117"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data_files = [\"bigdf1.csv\", \"bigdf.bin\", \"bigdf.arrow\", \"bigdf1.json\", \"bigdf2.json\"]\n",
    "df = DataFrame(file = data_files, size = getfield.(stat.(data_files), :size))\n",
    "append!(df, DataFrame(file = \"bigdf.jdf\", size=reduce((x,y)->x+y.size,\n",
    "                                                      stat.(joinpath.(\"bigdf.jdf\", readdir(\"bigdf.jdf\"))),\n",
    "                                                      init=0)))\n",
    "sort!(df, :size)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
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      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "@df df plot(:file, :size/1024^2, seriestype=:bar, title = \"Format File Size (MB)\", label=\"Size\", ylab=\"MB\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "First run\n",
      "CSV.jl\n",
      "  2.607400 seconds (928.84 k allocations: 202.391 MiB)\n",
      "Serialization\n",
      "  2.710477 seconds (49.56 M allocations: 806.986 MiB, 11.29% gc time)\n",
      "JDF.jl\n",
      "  0.676249 seconds (192.12 k allocations: 156.729 MiB, 9.14% gc time)\n",
      "JLSO.jl\n",
      "  1.975146 seconds (49.68 M allocations: 818.153 MiB, 6.09% gc time)\n",
      "Arrow.jl\n",
      "  0.193021 seconds (287.39 k allocations: 14.699 MiB)\n",
      "  1.084494 seconds (335.84 k allocations: 66.043 MiB)\n",
      "JSONTables.jl arraytable\n",
      "  4.053238 seconds (27 allocations: 2.144 GiB, 6.48% gc time)\n",
      "JSONTables.jl objecttable\n",
      "  2.621731 seconds (2.50 k allocations: 1.156 GiB, 11.33% gc time)\n",
      "Second run\n",
      "  2.314200 seconds (6.22 k allocations: 155.171 MiB, 4.05% gc time)\n",
      "Serialization\n",
      "  2.343229 seconds (49.55 M allocations: 806.356 MiB, 3.73% gc time)\n",
      "JDF.jl\n",
      "  0.449178 seconds (34.62 k allocations: 148.844 MiB, 1.13% gc time)\n",
      "JLSO.jl\n",
      "  1.868072 seconds (49.67 M allocations: 817.597 MiB, 4.71% gc time)\n",
      "Arrow.jl\n",
      "  0.002340 seconds (31.11 k allocations: 1.693 MiB)\n",
      "  0.905664 seconds (101.53 k allocations: 53.615 MiB)\n",
      "JSONTables.jl arraytable\n",
      "  4.051027 seconds (27 allocations: 2.144 GiB, 5.96% gc time)\n",
      "JSONTables.jl objecttable\n",
      "  2.641381 seconds (40 allocations: 1.156 GiB, 11.36% gc time)\n"
     ]
    }
   ],
   "source": [
    "println(\"First run\")\n",
    "println(\"CSV.jl\")\n",
    "csvread1 = @elapsed @time CSV.read(\"bigdf1.csv\", DataFrame)\n",
    "println(\"Serialization\")\n",
    "serializeread1 = @elapsed @time open(deserialize, \"bigdf.bin\")\n",
    "println(\"JDF.jl\")\n",
    "jdfread1 = @elapsed @time loadjdf(\"bigdf.jdf\")\n",
    "println(\"JLSO.jl\")\n",
    "jlsoread1 = @elapsed @time JLSO.load(\"bigdf.jlso\")\n",
    "println(\"Arrow.jl\")\n",
    "arrowread1 = @elapsed @time df_tmp = Arrow.Table(\"bigdf.arrow\") |> DataFrame\n",
    "arrowread1copy = @elapsed @time copy(df_tmp)\n",
    "println(\"JSONTables.jl arraytable\")\n",
    "jsontablesaread1 = @elapsed @time open(jsontable, \"bigdf1.json\")\n",
    "println(\"JSONTables.jl objecttable\")\n",
    "jsontablesoread1 = @elapsed @time open(jsontable, \"bigdf2.json\")\n",
    "println(\"Second run\")\n",
    "csvread2 = @elapsed @time CSV.read(\"bigdf1.csv\", DataFrame)\n",
    "println(\"Serialization\")\n",
    "serializeread2 = @elapsed @time open(deserialize, \"bigdf.bin\")\n",
    "println(\"JDF.jl\")\n",
    "jdfread2 = @elapsed @time loadjdf(\"bigdf.jdf\")\n",
    "println(\"JLSO.jl\")\n",
    "jlsoread2 = @elapsed @time JLSO.load(\"bigdf.jlso\")\n",
    "println(\"Arrow.jl\")\n",
    "arrowread2 = @elapsed @time df_tmp = Arrow.Table(\"bigdf.arrow\") |> DataFrame\n",
    "arrowread2copy = @elapsed @time copy(df_tmp)\n",
    "println(\"JSONTables.jl arraytable\")\n",
    "jsontablesaread2 = @elapsed @time open(jsontable, \"bigdf1.json\")\n",
    "println(\"JSONTables.jl objecttable\")\n",
    "jsontablesoread2 = @elapsed @time open(jsontable, \"bigdf2.json\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
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      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "groupedbar(\n",
    "    repeat([\"CSV.jl\", \"Serialization\", \"JDF.jl\", \"JLSO.jl\", \"Arrow.jl\", \"Arrow.jl\\ncopy\", \"JSON\\narraytable\",\n",
    "            \"JSON\\nobjecttable\"], inner = 2),\n",
    "    [csvread1, csvread2, serializeread1, serializeread2, jdfread1, jdfread2, jlsoread1, jlsoread2,\n",
    "     arrowread1, arrowread2, arrowread1+arrowread1copy, arrowread2+arrowread2copy,\n",
    "     jsontablesaread1, jsontablesaread2, jsontablesoread1, jsontablesoread2],    \n",
    "    group = repeat([\"1st\", \"2nd\"], outer = 8),\n",
    "    ylab = \"Second\",\n",
    "    title = \"Read Performance\\nDataFrame: bigdf\\nSize: $(size(bigdf))\"\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using gzip compression"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A common user requirement is to be able to load and save CSV that are compressed using gzip.\n",
    "Below we show how this can be accomplished using CodecZlib.jl.\n",
    "The same pattern is applicable to JSONTables.jl compression/decompression.\n",
    "\n",
    "Again make sure that you do not have file named `df_compress_test.csv.gz` in your working directory"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We first generate a random data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>x1</th><th>x2</th><th>x3</th><th>x4</th><th>x5</th><th>x6</th><th>x7</th><th>x8</th><th>x9</th><th>x10</th><th>x11</th><th>x12</th></tr><tr><th></th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th></tr></thead><tbody><p>10 rows × 1,000 columns (omitted printing of 988 columns)</p><tr><th>1</th><td>10</td><td>2</td><td>9</td><td>9</td><td>2</td><td>4</td><td>8</td><td>6</td><td>1</td><td>7</td><td>8</td><td>1</td></tr><tr><th>2</th><td>2</td><td>6</td><td>2</td><td>8</td><td>3</td><td>8</td><td>6</td><td>3</td><td>10</td><td>5</td><td>5</td><td>1</td></tr><tr><th>3</th><td>1</td><td>5</td><td>3</td><td>7</td><td>5</td><td>7</td><td>7</td><td>1</td><td>5</td><td>7</td><td>10</td><td>7</td></tr><tr><th>4</th><td>9</td><td>10</td><td>10</td><td>7</td><td>1</td><td>5</td><td>6</td><td>5</td><td>9</td><td>6</td><td>7</td><td>10</td></tr><tr><th>5</th><td>1</td><td>10</td><td>8</td><td>7</td><td>5</td><td>8</td><td>2</td><td>1</td><td>10</td><td>9</td><td>5</td><td>1</td></tr><tr><th>6</th><td>2</td><td>6</td><td>7</td><td>8</td><td>10</td><td>4</td><td>4</td><td>4</td><td>3</td><td>8</td><td>5</td><td>5</td></tr><tr><th>7</th><td>8</td><td>7</td><td>10</td><td>8</td><td>9</td><td>2</td><td>3</td><td>3</td><td>5</td><td>2</td><td>9</td><td>7</td></tr><tr><th>8</th><td>5</td><td>8</td><td>4</td><td>6</td><td>7</td><td>6</td><td>3</td><td>4</td><td>8</td><td>7</td><td>7</td><td>8</td></tr><tr><th>9</th><td>8</td><td>8</td><td>5</td><td>1</td><td>5</td><td>5</td><td>6</td><td>5</td><td>4</td><td>8</td><td>4</td><td>7</td></tr><tr><th>10</th><td>2</td><td>9</td><td>4</td><td>8</td><td>6</td><td>7</td><td>2</td><td>5</td><td>1</td><td>9</td><td>6</td><td>1</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccccccccccccc}\n",
       "\t& x1 & x2 & x3 & x4 & x5 & x6 & x7 & x8 & x9 & x10 & x11 & x12 & \\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & \\\\\n",
       "\t\\hline\n",
       "\t1 & 10 & 2 & 9 & 9 & 2 & 4 & 8 & 6 & 1 & 7 & 8 & 1 & $\\dots$ \\\\\n",
       "\t2 & 2 & 6 & 2 & 8 & 3 & 8 & 6 & 3 & 10 & 5 & 5 & 1 & $\\dots$ \\\\\n",
       "\t3 & 1 & 5 & 3 & 7 & 5 & 7 & 7 & 1 & 5 & 7 & 10 & 7 & $\\dots$ \\\\\n",
       "\t4 & 9 & 10 & 10 & 7 & 1 & 5 & 6 & 5 & 9 & 6 & 7 & 10 & $\\dots$ \\\\\n",
       "\t5 & 1 & 10 & 8 & 7 & 5 & 8 & 2 & 1 & 10 & 9 & 5 & 1 & $\\dots$ \\\\\n",
       "\t6 & 2 & 6 & 7 & 8 & 10 & 4 & 4 & 4 & 3 & 8 & 5 & 5 & $\\dots$ \\\\\n",
       "\t7 & 8 & 7 & 10 & 8 & 9 & 2 & 3 & 3 & 5 & 2 & 9 & 7 & $\\dots$ \\\\\n",
       "\t8 & 5 & 8 & 4 & 6 & 7 & 6 & 3 & 4 & 8 & 7 & 7 & 8 & $\\dots$ \\\\\n",
       "\t9 & 8 & 8 & 5 & 1 & 5 & 5 & 6 & 5 & 4 & 8 & 4 & 7 & $\\dots$ \\\\\n",
       "\t10 & 2 & 9 & 4 & 8 & 6 & 7 & 2 & 5 & 1 & 9 & 6 & 1 & $\\dots$ \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m10×1000 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x1    \u001b[0m\u001b[1m x2    \u001b[0m\u001b[1m x3    \u001b[0m\u001b[1m x4    \u001b[0m\u001b[1m x5    \u001b[0m\u001b[1m x6    \u001b[0m\u001b[1m x7    \u001b[0m\u001b[1m x8    \u001b[0m\u001b[1m x9    \u001b[0m\u001b[1m x10   \u001b[0m\u001b[1m x\u001b[0m ⋯\n",
       "\u001b[1m     \u001b[0m│\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m I\u001b[0m ⋯\n",
       "─────┼──────────────────────────────────────────────────────────────────────────\n",
       "   1 │    10      2      9      9      2      4      8      6  1      7      8 ⋯\n",
       "   2 │     2      6      2      8      3      8      6      3  10     5      5\n",
       "   3 │     1      5      3      7      5      7      7      1  5      7      1\n",
       "   4 │     9     10     10      7      1      5      6      5  9      6      7\n",
       "   5 │     1     10      8      7      5      8      2      1  10     9      5 ⋯\n",
       "   6 │     2      6      7      8     10      4      4      4  3      8      5\n",
       "   7 │     8      7     10      8      9      2      3      3  5      2      9\n",
       "   8 │     5      8      4      6      7      6      3      4  8      7      7\n",
       "   9 │     8      8      5      1      5      5      6      5  4      8      4 ⋯\n",
       "  10 │     2      9      4      8      6      7      2      5  1      9      6\n",
       "\u001b[31m                                                             990 columns omitted\u001b[0m"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = DataFrame(rand(1:10, 10, 1000), :auto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [],
   "source": [
    "# GzipCompressorStream comes from CodecZlib\n",
    "\n",
    "open(\"df_compress_test.csv.gz\", \"w\") do io\n",
    "    stream = GzipCompressorStream(io)\n",
    "    CSV.write(stream, df)\n",
    "    close(stream)\n",
    "end"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>x1</th><th>x2</th><th>x3</th><th>x4</th><th>x5</th><th>x6</th><th>x7</th><th>x8</th><th>x9</th><th>x10</th><th>x11</th><th>x12</th></tr><tr><th></th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th></tr></thead><tbody><p>10 rows × 1,000 columns (omitted printing of 988 columns)</p><tr><th>1</th><td>10</td><td>2</td><td>9</td><td>9</td><td>2</td><td>4</td><td>8</td><td>6</td><td>1</td><td>7</td><td>8</td><td>1</td></tr><tr><th>2</th><td>2</td><td>6</td><td>2</td><td>8</td><td>3</td><td>8</td><td>6</td><td>3</td><td>10</td><td>5</td><td>5</td><td>1</td></tr><tr><th>3</th><td>1</td><td>5</td><td>3</td><td>7</td><td>5</td><td>7</td><td>7</td><td>1</td><td>5</td><td>7</td><td>10</td><td>7</td></tr><tr><th>4</th><td>9</td><td>10</td><td>10</td><td>7</td><td>1</td><td>5</td><td>6</td><td>5</td><td>9</td><td>6</td><td>7</td><td>10</td></tr><tr><th>5</th><td>1</td><td>10</td><td>8</td><td>7</td><td>5</td><td>8</td><td>2</td><td>1</td><td>10</td><td>9</td><td>5</td><td>1</td></tr><tr><th>6</th><td>2</td><td>6</td><td>7</td><td>8</td><td>10</td><td>4</td><td>4</td><td>4</td><td>3</td><td>8</td><td>5</td><td>5</td></tr><tr><th>7</th><td>8</td><td>7</td><td>10</td><td>8</td><td>9</td><td>2</td><td>3</td><td>3</td><td>5</td><td>2</td><td>9</td><td>7</td></tr><tr><th>8</th><td>5</td><td>8</td><td>4</td><td>6</td><td>7</td><td>6</td><td>3</td><td>4</td><td>8</td><td>7</td><td>7</td><td>8</td></tr><tr><th>9</th><td>8</td><td>8</td><td>5</td><td>1</td><td>5</td><td>5</td><td>6</td><td>5</td><td>4</td><td>8</td><td>4</td><td>7</td></tr><tr><th>10</th><td>2</td><td>9</td><td>4</td><td>8</td><td>6</td><td>7</td><td>2</td><td>5</td><td>1</td><td>9</td><td>6</td><td>1</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|ccccccccccccc}\n",
       "\t& x1 & x2 & x3 & x4 & x5 & x6 & x7 & x8 & x9 & x10 & x11 & x12 & \\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & Int64 & \\\\\n",
       "\t\\hline\n",
       "\t1 & 10 & 2 & 9 & 9 & 2 & 4 & 8 & 6 & 1 & 7 & 8 & 1 & $\\dots$ \\\\\n",
       "\t2 & 2 & 6 & 2 & 8 & 3 & 8 & 6 & 3 & 10 & 5 & 5 & 1 & $\\dots$ \\\\\n",
       "\t3 & 1 & 5 & 3 & 7 & 5 & 7 & 7 & 1 & 5 & 7 & 10 & 7 & $\\dots$ \\\\\n",
       "\t4 & 9 & 10 & 10 & 7 & 1 & 5 & 6 & 5 & 9 & 6 & 7 & 10 & $\\dots$ \\\\\n",
       "\t5 & 1 & 10 & 8 & 7 & 5 & 8 & 2 & 1 & 10 & 9 & 5 & 1 & $\\dots$ \\\\\n",
       "\t6 & 2 & 6 & 7 & 8 & 10 & 4 & 4 & 4 & 3 & 8 & 5 & 5 & $\\dots$ \\\\\n",
       "\t7 & 8 & 7 & 10 & 8 & 9 & 2 & 3 & 3 & 5 & 2 & 9 & 7 & $\\dots$ \\\\\n",
       "\t8 & 5 & 8 & 4 & 6 & 7 & 6 & 3 & 4 & 8 & 7 & 7 & 8 & $\\dots$ \\\\\n",
       "\t9 & 8 & 8 & 5 & 1 & 5 & 5 & 6 & 5 & 4 & 8 & 4 & 7 & $\\dots$ \\\\\n",
       "\t10 & 2 & 9 & 4 & 8 & 6 & 7 & 2 & 5 & 1 & 9 & 6 & 1 & $\\dots$ \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m10×1000 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x1    \u001b[0m\u001b[1m x2    \u001b[0m\u001b[1m x3    \u001b[0m\u001b[1m x4    \u001b[0m\u001b[1m x5    \u001b[0m\u001b[1m x6    \u001b[0m\u001b[1m x7    \u001b[0m\u001b[1m x8    \u001b[0m\u001b[1m x9    \u001b[0m\u001b[1m x10   \u001b[0m\u001b[1m x\u001b[0m ⋯\n",
       "\u001b[1m     \u001b[0m│\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m I\u001b[0m ⋯\n",
       "─────┼──────────────────────────────────────────────────────────────────────────\n",
       "   1 │    10      2      9      9      2      4      8      6  1      7      8 ⋯\n",
       "   2 │     2      6      2      8      3      8      6      3  10     5      5\n",
       "   3 │     1      5      3      7      5      7      7      1  5      7      1\n",
       "   4 │     9     10     10      7      1      5      6      5  9      6      7\n",
       "   5 │     1     10      8      7      5      8      2      1  10     9      5 ⋯\n",
       "   6 │     2      6      7      8     10      4      4      4  3      8      5\n",
       "   7 │     8      7     10      8      9      2      3      3  5      2      9\n",
       "   8 │     5      8      4      6      7      6      3      4  8      7      7\n",
       "   9 │     8      8      5      1      5      5      6      5  4      8      4 ⋯\n",
       "  10 │     2      9      4      8      6      7      2      5  1      9      6\n",
       "\u001b[31m                                                             990 columns omitted\u001b[0m"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = CSV.File(transcode(GzipDecompressor, Mmap.mmap(\"df_compress_test.csv.gz\"))) |> DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df == df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using zip files"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes you may have files compressed inside a zip file.\n",
    "\n",
    "In such a situation you may use [ZipFile.jl](https://github.com/fhs/ZipFile.jl) in conjunction an an appropriate reader to read the files."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Here we first create a ZIP file and then read back its contents into a `DataFrame`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>x1</th><th>x2</th><th>x3</th><th>x4</th></tr><tr><th></th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th></tr></thead><tbody><p>3 rows × 4 columns</p><tr><th>1</th><td>9</td><td>10</td><td>9</td><td>1</td></tr><tr><th>2</th><td>4</td><td>1</td><td>1</td><td>6</td></tr><tr><th>3</th><td>1</td><td>5</td><td>8</td><td>10</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& x1 & x2 & x3 & x4\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & Int64 & Int64\\\\\n",
       "\t\\hline\n",
       "\t1 & 9 & 10 & 9 & 1 \\\\\n",
       "\t2 & 4 & 1 & 1 & 6 \\\\\n",
       "\t3 & 1 & 5 & 8 & 10 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x1    \u001b[0m\u001b[1m x2    \u001b[0m\u001b[1m x3    \u001b[0m\u001b[1m x4    \u001b[0m\n",
       "\u001b[1m     \u001b[0m│\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\n",
       "─────┼────────────────────────────\n",
       "   1 │     9     10      9      1\n",
       "   2 │     4      1      1      6\n",
       "   3 │     1      5      8     10"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = DataFrame(rand(1:10, 3, 4), :auto)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>x1</th><th>x2</th><th>x3</th><th>x4</th></tr><tr><th></th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th></tr></thead><tbody><p>3 rows × 4 columns</p><tr><th>1</th><td>3</td><td>4</td><td>2</td><td>2</td></tr><tr><th>2</th><td>10</td><td>3</td><td>9</td><td>3</td></tr><tr><th>3</th><td>7</td><td>7</td><td>8</td><td>6</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& x1 & x2 & x3 & x4\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & Int64 & Int64\\\\\n",
       "\t\\hline\n",
       "\t1 & 3 & 4 & 2 & 2 \\\\\n",
       "\t2 & 10 & 3 & 9 & 3 \\\\\n",
       "\t3 & 7 & 7 & 8 & 6 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x1    \u001b[0m\u001b[1m x2    \u001b[0m\u001b[1m x3    \u001b[0m\u001b[1m x4    \u001b[0m\n",
       "\u001b[1m     \u001b[0m│\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\n",
       "─────┼────────────────────────────\n",
       "   1 │     3      4      2      2\n",
       "   2 │    10      3      9      3\n",
       "   3 │     7      7      8      6"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2 = DataFrame(rand(1:10, 3, 4), :auto)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "And we show yet another way to write a `DataFrame` into a CSV file"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "# write a CSV file into the zip file\n",
    "w = ZipFile.Writer(\"x.zip\")\n",
    "\n",
    "f1 = ZipFile.addfile(w, \"x1.csv\")\n",
    "write(f1, sprint(show, \"text/csv\", df1))\n",
    "\n",
    "# write a second CSV file into zip file\n",
    "f2 = ZipFile.addfile(w, \"x2.csv\", method=ZipFile.Deflate)\n",
    "write(f2, sprint(show, \"text/csv\", df2))\n",
    "\n",
    "close(w)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we read the CSV we have written:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "z = ZipFile.Reader(\"x.zip\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>x1</th><th>x2</th><th>x3</th><th>x4</th></tr><tr><th></th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th></tr></thead><tbody><p>3 rows × 4 columns</p><tr><th>1</th><td>9</td><td>10</td><td>9</td><td>1</td></tr><tr><th>2</th><td>4</td><td>1</td><td>1</td><td>6</td></tr><tr><th>3</th><td>1</td><td>5</td><td>8</td><td>10</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& x1 & x2 & x3 & x4\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & Int64 & Int64\\\\\n",
       "\t\\hline\n",
       "\t1 & 9 & 10 & 9 & 1 \\\\\n",
       "\t2 & 4 & 1 & 1 & 6 \\\\\n",
       "\t3 & 1 & 5 & 8 & 10 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x1    \u001b[0m\u001b[1m x2    \u001b[0m\u001b[1m x3    \u001b[0m\u001b[1m x4    \u001b[0m\n",
       "\u001b[1m     \u001b[0m│\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\n",
       "─────┼────────────────────────────\n",
       "   1 │     9     10      9      1\n",
       "   2 │     4      1      1      6\n",
       "   3 │     1      5      8     10"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the index index of file called x1.csv\n",
    "index_xcsv = findfirst(x->x.name == \"x1.csv\", z.files)\n",
    "# to read the x1.csv file in the zip file\n",
    "df1_2 = CSV.read(read(z.files[index_xcsv]), DataFrame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1_2 == df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><thead><tr><th></th><th>x1</th><th>x2</th><th>x3</th><th>x4</th></tr><tr><th></th><th>Int64</th><th>Int64</th><th>Int64</th><th>Int64</th></tr></thead><tbody><p>3 rows × 4 columns</p><tr><th>1</th><td>3</td><td>4</td><td>2</td><td>2</td></tr><tr><th>2</th><td>10</td><td>3</td><td>9</td><td>3</td></tr><tr><th>3</th><td>7</td><td>7</td><td>8</td><td>6</td></tr></tbody></table>"
      ],
      "text/latex": [
       "\\begin{tabular}{r|cccc}\n",
       "\t& x1 & x2 & x3 & x4\\\\\n",
       "\t\\hline\n",
       "\t& Int64 & Int64 & Int64 & Int64\\\\\n",
       "\t\\hline\n",
       "\t1 & 3 & 4 & 2 & 2 \\\\\n",
       "\t2 & 10 & 3 & 9 & 3 \\\\\n",
       "\t3 & 7 & 7 & 8 & 6 \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "\u001b[1m3×4 DataFrame\u001b[0m\n",
       "\u001b[1m Row \u001b[0m│\u001b[1m x1    \u001b[0m\u001b[1m x2    \u001b[0m\u001b[1m x3    \u001b[0m\u001b[1m x4    \u001b[0m\n",
       "\u001b[1m     \u001b[0m│\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m Int64 \u001b[0m\n",
       "─────┼────────────────────────────\n",
       "   1 │     3      4      2      2\n",
       "   2 │    10      3      9      3\n",
       "   3 │     7      7      8      6"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# find the index index of file called x2.csv\n",
    "index_xcsv = findfirst(x->x.name == \"x2.csv\", z.files)\n",
    "# to read the x2.csv file in the zip file\n",
    "df2_2 = CSV.read(read(z.files[index_xcsv]), DataFrame)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "true"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2_2 == df2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that once you read a given file from `z` object its stream is all used-up (it is at its end). Therefore to read it again you need to close `z` and open it again.\n",
    "\n",
    "Also do not forget to close the zip file once done."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [],
   "source": [
    "close(z)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Finally, let's clean up. Do not run the next cell unless you are sure that it will not erase your important files."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [],
   "source": [
    "foreach(rm, [\"x1.csv\", \"x.bin\", \"x.jlso\", \"x1.json\", \"x2.json\",\n",
    "             \"bigdf1.csv\", \"bigdf.bin\", \"bigdf.jlso\", \"bigdf1.json\", \"bigdf2.json\", \n",
    "             \"x.zip\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [],
   "source": [
    "rm(\"bigdf.jdf\", recursive=true)\n",
    "rm(\"x.jdf\", recursive=true)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Note that we did not remove `x.arrow` and `bigdf.arrow` and `df_compress_test.csv.gz` - you have to do it manually, as these files are memory mapped."
   ]
  }
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